{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "02. Neural network classification with TensorFlow Exercise Solutions.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashikshafi08/Learning_Tensorflow/blob/main/Exercise%20Solutions/%F0%9F%9B%A0%2002_neural_network_classification_in_tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LQL6qabicC6"
      },
      "source": [
        "# 02. Neural network classification with TensorFlow Exercise Solutions\n",
        "\n",
        "## Questionnaire \n",
        "\n",
        "1. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.\n",
        "\n",
        "2. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas.\n",
        "\n",
        "3. Recreate [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax)'s [softmax activation](https://en.wikipedia.org/wiki/Softmax_function) function in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.\n",
        "\n",
        "4. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.\n",
        "\n",
        "5. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the T-shirt class with their predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyzC2Djhm66c"
      },
      "source": [
        "# Importing the stuffs we need \n",
        "import tensorflow as tf \n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxcZM8D4khjL"
      },
      "source": [
        "### 1. Create a classification dataset using Scikit-Learn's [`make_moons()`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html) function, visualize it and then build a model to fit it at over 85% accuracy.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "cf-sqTjJlPvf",
        "outputId": "92c7876f-8887-46c6-e0bd-4ce0e6a37898"
      },
      "source": [
        "# Importing the make moons from sklearn datasets \n",
        "from sklearn.datasets import make_moons\n",
        "\n",
        "# Make 3k samples \n",
        "n_samples = 1000\n",
        "\n",
        "# Create circles \n",
        "X , y = make_moons(n_samples = n_samples ,\n",
        "                   random_state = 42)\n",
        "\n",
        "# Checking the shape of X and y \n",
        "print(X.shape , y.shape)\n",
        "\n",
        "# Packing them into a dataframe \n",
        "make_moons_df = pd.DataFrame({'col_1': X[: , 0] , \n",
        "                              'col_2': X[: , 1], \n",
        "                              'label': y})\n",
        "make_moons_df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1000, 2) (1000,)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>col_1</th>\n",
              "      <th>col_2</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.008727</td>\n",
              "      <td>0.368174</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.921384</td>\n",
              "      <td>-0.496905</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.940226</td>\n",
              "      <td>-0.498212</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.465875</td>\n",
              "      <td>-0.345406</td>\n",
              "      <td>1</td>\n",
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            ],
            "text/plain": [
              "      col_1     col_2  label\n",
              "0  0.008727  0.368174      1\n",
              "1  0.921384 -0.496905      1\n",
              "2  0.940226 -0.498212      1\n",
              "3  0.465875 -0.345406      1\n",
              "4 -0.850412  0.526117      0"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "JZUY3gernLu1",
        "outputId": "290ba7c4-c048-4efc-f153-c49684682c5e"
      },
      "source": [
        "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.RdYlBu);"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BTQbuxDBoFZw",
        "outputId": "9ac9a464-8819-4517-982a-56340dda5179"
      },
      "source": [
        "# Splitting the dataset into train and test \n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train , X_test , y_train , y_test = train_test_split(X , y , \n",
        "                                                       test_size = 0.2 , \n",
        "                                                       random_state = 42)\n",
        "\n",
        "# Checking the shape after splitting \n",
        "X_train.shape , X_test.shape , y_train.shape , y_test.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((800, 2), (200, 2), (800,), (200,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8X78NCMpfrg",
        "outputId": "42ce3883-2a1b-4cfc-e644-dd45536de048"
      },
      "source": [
        "# Building a simple model \n",
        "from tensorflow.keras import layers \n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "    layers.Dense(32 , activation= 'relu'), \n",
        "    layers.Dense(10 , activation= 'relu'),\n",
        "    #layers.Flatten(),\n",
        "    layers.Dense(1 , activation= 'sigmoid')\n",
        "])\n",
        "\n",
        "# Compiling the model \n",
        "model.compile(loss = tf.keras.losses.BinaryCrossentropy() , \n",
        "              optimizer = tf.keras.optimizers.Adam() , \n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "# Fitting the model \n",
        "model.fit(X_train , y_train, \n",
        "          epochs = 15)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "25/25 [==============================] - 1s 2ms/step - loss: 0.6418 - accuracy: 0.7700\n",
            "Epoch 2/15\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.5749 - accuracy: 0.8075\n",
            "Epoch 3/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.5063 - accuracy: 0.8200\n",
            "Epoch 4/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.4385 - accuracy: 0.8338\n",
            "Epoch 5/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3798 - accuracy: 0.8462\n",
            "Epoch 6/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.3363 - accuracy: 0.8537\n",
            "Epoch 7/15\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.3077 - accuracy: 0.8600\n",
            "Epoch 8/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2882 - accuracy: 0.8662\n",
            "Epoch 9/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2737 - accuracy: 0.8712\n",
            "Epoch 10/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2618 - accuracy: 0.8750\n",
            "Epoch 11/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2510 - accuracy: 0.8800\n",
            "Epoch 12/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2423 - accuracy: 0.8888\n",
            "Epoch 13/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2344 - accuracy: 0.8888\n",
            "Epoch 14/15\n",
            "25/25 [==============================] - 0s 1ms/step - loss: 0.2269 - accuracy: 0.8913\n",
            "Epoch 15/15\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2211 - accuracy: 0.8925\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f29554da210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwEUzqdkpyL_"
      },
      "source": [
        "# Creating a function to visualize how our model is performing...\n",
        "def plot_decision_boundary(model, X, y):\n",
        "  \"\"\"\n",
        "  Plots the decision boundary created by a model predicting on X.\n",
        "\n",
        "  Arguments: \n",
        "    model --> the trained model \n",
        "    X --> the feature data (numpy array)\n",
        "    y --> the truth labels \n",
        "\n",
        "  Returns:\n",
        "    A visualization of how our model is performing (fitting with our data)\n",
        "    \n",
        "  \"\"\"\n",
        "\n",
        "  # Define the axis boundaries of the plot and create a meshgrid\n",
        "  x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n",
        "  y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n",
        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
        "                       np.linspace(y_min, y_max, 100))\n",
        "  \n",
        "  # Create X values (we're going to predict on all of these)\n",
        "  x_in = np.c_[xx.ravel(), yy.ravel()] # stack 2D arrays together: https://numpy.org/devdocs/reference/generated/numpy.c_.html\n",
        "  \n",
        "  # Make predictions using the trained model\n",
        "  y_pred = model.predict(x_in)\n",
        "\n",
        "  # Check for multi-class\n",
        "  if len(y_pred[0]) > 1:\n",
        "    print(\"doing multiclass classification...\")\n",
        "    # We have to reshape our predictions to get them ready for plotting\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "  else:\n",
        "    print(\"doing binary classifcation...\")\n",
        "    y_pred = np.round(y_pred).reshape(xx.shape)\n",
        "  \n",
        "  # Plot decision boundary\n",
        "  plt.contourf(xx, yy, y_pred, cmap=plt.cm.RdYlBu, alpha=0.7)\n",
        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "FJOIDNxjr_S6",
        "outputId": "6a22668c-c0c8-4cbe-9dd9-12d607ac9eb1"
      },
      "source": [
        "plot_decision_boundary(model , X , y)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "doing binary classifcation...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q_VVcHOKsBia"
      },
      "source": [
        "Better results can be achieved if it's trained for more epoch. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-zPpGTlsYQf"
      },
      "source": [
        "### 2. Create a function (or write code) to visualize multiple image predictions for the fashion MNIST at the same time. Plot at least three different images and their prediciton labels at the same time. Hint: see the [classifcation tutorial in the TensorFlow documentation](https://www.tensorflow.org/tutorials/keras/classification) for ideas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AGhii-P-xepn",
        "outputId": "65e659ab-790d-444a-a12d-36305f1512b9"
      },
      "source": [
        "# Getting the MNIST data from the keras dataset\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "# Splitting into train and test \n",
        "(train_images , train_labels) , (test_images , test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "# Checking the shapes of the splits \n",
        "train_images.shape , train_labels.shape , test_images.shape , test_labels.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkssoDOeyCU8"
      },
      "source": [
        "### 3. Recreate [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/activations/softmax)'s [softmax activation](https://en.wikipedia.org/wiki/Softmax_function) function in your own code. Make sure it can accept a tensor and return that tensor after having the softmax function applied to it.\n",
        "\n",
        "![Screenshot 2021-06-28 at 5.37.01 AM.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ7eqeJ5z4XD"
      },
      "source": [
        "# Implementing a softmax function \n",
        "def softmax(x):\n",
        "  '''\n",
        "  Argument:\n",
        "  x --> Accepts a tensor of any shape\n",
        "\n",
        "  Returns: \n",
        "  --> softmax activations of the input tensor\n",
        "  '''\n",
        "  x = tf.cast(x , dtype = tf.float32) # to tackle the data type error of int32 / int64\n",
        "\n",
        "  # Below is the forumale\n",
        "  e_x = tf.math.exp(x - tf.math.reduce_max(x))\n",
        "  return e_x / tf.math.reduce_sum(e_x , axis = 0)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktu8BZVE3JKS",
        "outputId": "0dbbeaf1-40db-4214-caa8-dca424de2b6c"
      },
      "source": [
        "# Creating sample tensor \n",
        "tensor = tf.constant([[1, 2, 3, 6],\n",
        "                     [2, 4, 5, 6],\n",
        "                     [3, 8, 7, 6]] )\n",
        "tensor"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 4), dtype=int32, numpy=\n",
              "array([[1, 2, 3, 6],\n",
              "       [2, 4, 5, 6],\n",
              "       [3, 8, 7, 6]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNXkue-g371N",
        "outputId": "efbfd08f-682d-4d38-a15b-0f2a7af971d3"
      },
      "source": [
        "# Applying our softmax function \n",
        "softmax(tensor)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(3, 4), dtype=float32, numpy=\n",
              "array([[0.09003057, 0.00242826, 0.01587624, 0.3333333 ],\n",
              "       [0.24472849, 0.01794253, 0.11731043, 0.3333333 ],\n",
              "       [0.66524094, 0.9796292 , 0.8668133 , 0.3333333 ]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7p66jq3R7qcB"
      },
      "source": [
        "### 4. Train a model to get 88%+ accuracy on the fashion MNIST test set. Plot a confusion matrix to see the results after.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rVji2Gaq-i5T",
        "outputId": "aae7de23-6c0c-48fd-ac0c-abcbdaa1ff0a"
      },
      "source": [
        "# Getting the MNIST data from the keras dataset\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "\n",
        "# Splitting into train and test \n",
        "(train_images , train_labels) , (test_images , test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "# Number of classes and class names\n",
        "num_classes = 10     # 10 labels/classes\n",
        "\n",
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
        "\n",
        "# Checking the shapes of the splits \n",
        "train_images.shape , train_labels.shape , test_images.shape , test_labels.shape"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGRyxGDy_DW9",
        "outputId": "cb1f86c0-f8f0-4091-fc43-3a10b5c85b85"
      },
      "source": [
        "# Building a simple model for the fashion mnist \n",
        "simple_model = tf.keras.Sequential([\n",
        "  layers.Flatten(input_shape = (28 , 28)), \n",
        "  layers.Dense(64 , activation= 'relu'), \n",
        "  layers.Dense(32 , activation = 'relu'),\n",
        "  layers.Dense(10 , activation='softmax')\n",
        "])\n",
        "\n",
        "# Compiling the model \n",
        "simple_model.compile(loss = tf.keras.losses.SparseCategoricalCrossentropy(), # because our labels isnt one hot encoded\n",
        "                     optimizer = tf.keras.optimizers.Adam() , \n",
        "                     metrics = ['accuracy'])\n",
        "\n",
        "# Fitting the model \n",
        "history = simple_model.fit(train_images , train_labels , \n",
        "                           epochs = 20)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 7s 3ms/step - loss: 2.1116 - accuracy: 0.6450\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.7286 - accuracy: 0.7228\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5617 - accuracy: 0.7955\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.5149 - accuracy: 0.8159\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4847 - accuracy: 0.8273\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4583 - accuracy: 0.8349\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4401 - accuracy: 0.8429\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4204 - accuracy: 0.8500\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.4093 - accuracy: 0.8539\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3982 - accuracy: 0.8588\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3941 - accuracy: 0.8610\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3746 - accuracy: 0.8658\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3722 - accuracy: 0.8676\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3699 - accuracy: 0.8682\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3606 - accuracy: 0.8709\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3554 - accuracy: 0.8733\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3542 - accuracy: 0.8740\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3532 - accuracy: 0.8744\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3469 - accuracy: 0.8743\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3454 - accuracy: 0.8761\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-zAd6NCAnmB"
      },
      "source": [
        "I have just trained for 20 epochs, by adding a extra dense layer (or) training for more epochs will able to give us great results. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IwFSjxxhB9Il"
      },
      "source": [
        "# Function for the confusion matrix \n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "# Our function needs a different name to sklearn's plot_confusion_matrix\n",
        "def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15): \n",
        "  \"\"\"Makes a labelled confusion matrix comparing predictions and ground truth labels.\n",
        "\n",
        "  If classes is passed, confusion matrix will be labelled, if not, integer class values\n",
        "  will be used.\n",
        "\n",
        "  Args:\n",
        "    y_true: Array of truth labels (must be same shape as y_pred).\n",
        "    y_pred: Array of predicted labels (must be same shape as y_true).\n",
        "    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.\n",
        "    figsize: Size of output figure (default=(10, 10)).\n",
        "    text_size: Size of output figure text (default=15).\n",
        "  \n",
        "  Returns:\n",
        "    A labelled confusion matrix plot comparing y_true and y_pred.\n",
        "\n",
        "  Example usage:\n",
        "    make_confusion_matrix(y_true=test_labels, # ground truth test labels\n",
        "                          y_pred=y_preds, # predicted labels\n",
        "                          classes=class_names, # array of class label names\n",
        "                          figsize=(15, 15),\n",
        "                          text_size=10)\n",
        "  \"\"\"  \n",
        "  # Create the confustion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis] # normalize it\n",
        "  n_classes = cm.shape[0] # find the number of classes we're dealing with\n",
        "\n",
        "  # Plot the figure and make it pretty\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Are there a list of classes?\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "  \n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "         xlabel=\"Predicted label\",\n",
        "         ylabel=\"True label\",\n",
        "         xticks=np.arange(n_classes), # create enough axis slots for each class\n",
        "         yticks=np.arange(n_classes), \n",
        "         xticklabels=labels, # axes will labeled with class names (if they exist) or ints\n",
        "         yticklabels=labels)\n",
        "  \n",
        "  # Make x-axis labels appear on bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max() + cm.min()) / 2.\n",
        "\n",
        "  # Plot the text on each cell\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)\",\n",
        "             horizontalalignment=\"center\",\n",
        "             color=\"white\" if cm[i, j] > threshold else \"black\",\n",
        "             size=text_size)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JubLp6X5CRfn",
        "outputId": "101f47cc-0481-43ae-ac0b-38278d189a03"
      },
      "source": [
        "# Making predictions with our model \n",
        "pred_probs = simple_model.predict(test_images)\n",
        "\n",
        "# Converting our pred probs to predictions \n",
        "preds = pred_probs.argmax(axis = 1)\n",
        "\n",
        "# Viewing the first 10 preds \n",
        "preds[:10]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "udjtfjM-C7tN",
        "outputId": "ecd0c23c-f225-48e9-eea2-335aaae4351f"
      },
      "source": [
        "# Plotting the confusion matrix \n",
        "make_confusion_matrix(y_true = test_labels , \n",
        "                      y_pred = preds , \n",
        "                      classes = class_names , \n",
        "                      figsize = (15 , 13), \n",
        "                      text_size = 10)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x936 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OfBhoENFDzKY",
        "outputId": "b6967c90-789c-4cc4-c26e-6c9d6835da55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class_names"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sB6ZFnLFFSGp"
      },
      "source": [
        "### 5. Make a function to show an image of a certain class of the fashion MNIST dataset and make a prediction on it. For example, plot 3 images of the T-shirt class with their predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hywcgtILP19F"
      },
      "source": [
        "# Making predictions \n",
        "preds = simple_model.predict(test_images)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ4-tX88GpSZ"
      },
      "source": [
        "# Below function is from TensorFlow Tutorials (https://www.tensorflow.org/tutorials/keras/classification#verify_predictions)\n",
        "\n",
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  true_label, img = true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  true_label = true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')\n",
        "\n",
        "\n",
        "\n",
        "def plot_prediction_images():\n",
        "  num_rows = 5 \n",
        "  num_cols = 3\n",
        "  num_images = num_rows * num_cols \n",
        "  plt.figure(figsize = (2*2*num_cols , 2*num_rows))\n",
        "  for i in range(num_images):\n",
        "    plt.subplot(num_rows , 2*num_cols , 2*i+1)\n",
        "    plot_image(i , preds[i], test_labels , test_images)\n",
        "    plt.subplot(num_rows , 2*num_cols , 2*i+2)\n",
        "    plot_value_array(i , preds[i] ,  test_labels)\n",
        "  plt.tight_layout()\n",
        "  plt.show\n",
        "\n"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqcedZh-MjnO",
        "outputId": "bbcb5ee4-cb85-4b2d-ffe8-9dbd24f2b9dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "# Using our function\n",
        "plot_prediction_images()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0dOJXcA3LkoV"
      },
      "source": [
        "Have a great day! "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziASxdMlQC9P"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}